@article {515, title = {Characterization of ion dynamics in structures for lossless ion manipulations.}, journal = {Anal Chem}, year = {2014}, abstract = {

Structures for Lossless Ion Manipulation (SLIM) represent a novel class of ion optical devices based upon electrodes patterned on planar surfaces, and relying on a combined action of radiofrequency and DC electric fields and specific buffer gas density conditions. Initial experimental studies have demonstrated the feasibility of the SLIM concept. This report offers an in-depth consideration of key ion dynamics properties in such devices based upon ion optics theory and computational modeling. The SLIM devices investigated are formed by two surfaces, each having an array of radiofrequency (RF) "rung" electrodes, bordered by DC "guard" electrodes. Ion motion is confined by the RF effective potential in the direction orthogonal to the boards and limited or controlled in the transversal direction by the guard DC potentials. Ions can be efficiently trapped and stored in SLIM devices where the confinement of ions can be "soft" in regard to the extent of collisional activation, similarly to RF-only multipole ion guides and traps. The segmentation of the RF rung electrodes and guards along the axis makes it possible to apply static or transient electric field profiles to stimulate ion transfer within a SLIM. In the case of a linear DC gradient applied to RF rungs and guards, a virtually uniform electric field can be created along the axis of the device, enabling high quality ion mobility separations.

}, keywords = {Electricity, Electrodes, Ions, Mass Spectrometry, Radio Waves}, doi = {10.1021/ac502054p}, author = {Tolmachev, Aleksey V and Webb, Ian K and Ibrahim, Yehia M and Garimella, Sandilya V B and Zhang, Xinyu and Anderson, Gordon A and Smith, Richard D} } @article {514, title = {Mobility-resolved ion selection in uniform drift field ion mobility spectrometry/mass spectrometry: dynamic switching in structures for lossless ion manipulations.}, journal = {Anal Chem}, year = {2014}, abstract = {


}, keywords = {Mass Spectrometry, Molecular Structure}, doi = {10.1021/ac502139e}, author = {Webb, Ian K and Garimella, Sandilya V B and Tolmachev, Aleksey V and Chen, Tsung-Chi and Zhang, Xinyu and Cox, Jonathan T and Norheim, Randolph V and Prost, Spencer A and LaMarche, Brian and Anderson, Gordon A and Ibrahim, Yehia M and Smith, Richard D} } @article {361, title = {Elimination of systematic mass measurement errors in liquid chromatography-mass spectrometry based proteomics using regression models and a priori partial knowledge of the sample content.}, journal = {Anal Chem}, year = {2008}, abstract = {

The high mass measurement accuracy and precision available with recently developed mass spectrometers is increasingly used in proteomics analyses to confidently identify tryptic peptides from complex mixtures of proteins, as well as post-translational modifications and peptides from nonannotated proteins. To take full advantage of high mass measurement accuracy instruments, it is necessary to limit systematic mass measurement errors. It is well known that errors in m/z measurements can be affected by experimental parameters that include, for example, outdated calibration coefficients, ion intensity, and temperature changes during the measurement. Traditionally, these variations have been corrected through the use of internal calibrants (well-characterized standards introduced with the sample being analyzed). In this paper, we describe an alternative approach where the calibration is provided through the use of a priori knowledge of the sample being analyzed. Such an approach has previously been demonstrated based on the dependence of systematic error on m/z alone. To incorporate additional explanatory variables, we employed multidimensional, nonparametric regression models, which were evaluated using several commercially available instruments. The applied approach is shown to remove any noticeable biases from the overall mass measurement errors and decreases the overall standard deviation of the mass measurement error distribution by 1.2-2-fold, depending on instrument type. Subsequent reduction of the random errors based on multiple measurements over consecutive spectra further improves accuracy and results in an overall decrease of the standard deviation by 1.8-3.7-fold. This new procedure will decrease the false discovery rates for peptide identifications using high-accuracy mass measurements.

}, keywords = {Algorithms, Calibration, Chromatography, Liquid, Complex Mixtures, False Positive Reactions, Mass Spectrometry, Models, Biological, Peptides, Protein Processing, Post-Translational, Proteomics, Regression Analysis, Reproducibility of Results, Sensitivity and Specificity, Trypsin}, doi = {10.1021/ac701863d}, author = {Petyuk, Vladislav A and Jaitly, Navdeep and Moore, Ronald J and Ding, Jie and Metz, Thomas O and Tang, Keqi and Matthew E. Monroe and Tolmachev, Aleksey V and Adkins, Joshua N and Belov, Mikhail E and Dabney, Alan R and Qian, Wei-Jun and Camp, David G and Smith, Richard D} }